SVM, ANFIS, regression and climate based models for reference evapotranspiration modeling using limited climatic data in a semi-arid highland environment

Abstract

Summary The accurate estimation of reference evapotranspiration (ETo) becomes imperative in the planning and management of irrigation practices. The Penman–Monteith FAO 56 (PMF-56) model which incorporates thermodynamic and aerodynamic aspects is recommended for estimating ETo across the world. However, the use of the PMF-56 model is restricted by the unavailability of input climatic variables in many locations and the option is to use simple approaches with limited data requirements. In the current study, the potential of support vector machines (SVM), adaptive neuro-fuzzy inference system (ANFIS), multiple linear regression (MLR) and multiple non-linear regression (MNLR) for estimating ETo were investigated using six input vectors of climatic data in a semi-arid highland environment in Iran. In addition, four temperature-based and eight radiation-based ETo equations were tested against the PMF-56 model. The accuracies of the models were evaluated by using three commonly used criteria: root mean square error (RMSE), mean absolute error (MAE) and correlation coefficient (r). The results obtained with the SVM and ANFIS models for ETo estimation were better than those achieved using the regression and climate based models and confirmed the ability of these techniques to provide useful tools in ETo modeling in semi-arid environments. Based on the comparison of the overall performances, it was found that the SVM6 and ANFIS6 models which require mean air temperature, relative humidity, wind speed and solar radiation input variables had the best accuracy.